Precise Phenotyping

A critical need in psychiatric practice is the availability of objective and quantitative measures for signs and symptoms of illness.  Currently, the reporting of signs and symptoms are solely based on patient reports and clinical observations, which are prone to a variety of biases including subjectivity and lack of consistency.  Objective measures could promote more accurate diagnosis and monitoring of illness course so that early and timely interventions can be implemented.  We are conducting pilot studies which are aimed towards developing new automated tools for characterizing two types of informative observable behaviors in schizophrenia:  speech and activity levels.

In collaboration with Daniel Jurafsky, a computational linguistist at Stanford, we are developing novel computer-automated algorithms for characterizing abnormal speech patterns in schizophrenia.

In collaboration with Jamie Zeitzer, PhD, specialist in circadian rhythms at Stanford, and Dr. Michael Hann, MD MBA MS, Assistant Professor in the Department of Psychiatry in the Uniformed Services University of the Health Sciences, we are utilizing continuous actigraphy to determine if variations in activity patterns can: 1) identify individuals with psychotic disorders, 2) distinguish between individuals with different types of psychotic disorders and 3) serve as early indicators for psychiatric decompensations.